Data Will Define the Future of Credit Scoring: Adapt or Fall behind

With the global datasphere projected to reach 175 zettabytes by 2025, and the rapid advancement of AI, credit scoring models can no longer simply rely on limited historical credit data. Integrating alternative data sources, AI-powered analysis and focusing on financial inclusion are paramount for its future. 
 
According to a recent study, modern credit scoring methods that include alternative data can potentially expand lenders’ new customer pools by 20%. Additionally, they promote financial inclusion by allowing the inclusion of individuals with thin or non-existent credit files.  
 
This article explores:  

• How alternative data and AI transform credit scoring  
• Challenges and key steps to adapt a data-driven strategy  
• How Cedar Rose can help you master the future of credit scoring  
  

How Alternative Data and AI transform Credit Scoring  

Alternative data uses non-traditional sources to evaluate a borrower’s creditworthiness. This involves a vast array of data like bank transaction history, online purchasing behaviour, social media profiles, phone usage, online behaviour, and employment history.  
 
While alternative data may be confused with metadata, it is different. 
As opposed to metadata, alternative data introduces completely new data sources into the credit scoring process. Metadata on the other hand simply add more context and extra information about the main credit data like data sources, verification methods, and timestamps.  
 
Types of alternative data for corporations are:  

• Web scraping data like customer reviews, website traffic and job postings that showcase a company’s growth trajectory and market engagement.  

• Satellite images like supply chain activity, or construction progress that provides evidence of a company’s operational efficiency and ability to scale.  

• IoT and Sensor data like manufacturing output, energy consumption, and equipment utilisation providing evidence of company’s efficiency and asset productivity. 

• Social Media data like public opinion which informs about company brand perception and reputation 

• Payment Data like supplier payment patterns that indicate liquidity and financial health, or customer payment behaviour that shows the quality of accounts receivable. 

• Market data like bond yields and stock price movements reflect market perception and perceived creditworthiness in debt markets.  

All in all, alternative data sources offer a more comprehensive and dynamic view of borrowers’ financial behaviour. They specifically enhance financial inclusion by allowing lenders to assess individuals or companies with limited financial history. By providing valuable insights into financial behaviour and considering a broader range of data points, they enhance risk assessment accuracy, and reduce biases that were present in traditional scoring models.  

Alternative data not only broadens lenders’ clientele, but also allows for customised borrower assessments and customised scoring models. By incorporating new parameters like bank transaction history, payment data from third party systems, unique scoring models can be developed to address specific business needs. Additionally, the real-time dynamic nature of alternative data makes it potentially more predictive for specific populations.  

It would be hard to talk about alternative data in credit scoring without bringing up AI and machine learning.  When combined AI and ML produce a potent synergy. Machine learning improves AI’s predictive powers, while AI improves machine learning models.  With its wider scope, AI uses unstructured data like news and customer reviews to simulate human thought processes and analyse complex data. Machine learning improves AI by finding trends and forecasting risk through the analysis of large data sets. Hence this results in more precise, flexible and personalised credit scores along with more dynamic adaptable risk monitoring.  
 
With that said, it’s no wonder that digital footprints are becoming a new goldmine for businesses. A company’s digital footprint refers to all its online assets, content and behaviour that can be seen by the public and the private internet. This can involve government websites, devices that connect to the internet, databases and what employees do.  

Challenges and Key Steps to Adapt a Data-Driven Strategy 

Despite the benefits alternative data and AI incorporation promises, applying it is not that easy.  Common challenges faced when incorporating AI and alternative data in credit scoring involve: 

• Data quality, consistency and privacy concerns  
  Since alternative data frequently comes in unstructured formats or varying standards, it is hard to handle and standardise. Ensuring data reliability and consistency is paramount to handling it. Moreover, integrating it into traditional credit information complicates the matter further because robust technology is needed to successfully merge different data sources. Additionally, access to alternative data might be difficult due to data owners’ reluctance to share their information, and strict data protection regulations (like GDPR or CCPA) across jurisdictions. 

• Regulatory and Ethical Compliance Challenges  
  The regulatory environment surrounding credit scoring is complex and constantly evolving, posing a challenge for companies using alternative data and AI. AI models must meet strict requirements for explainability and transparency, while adhering to privacy and data protection measures to avoid legal and reputational issues. Addressing bias in AI models is crucial for ethical lending practices, as hidden biases in historical data can skew results and lead to unfair outcomes. The complexity of AI algorithms can also create trust issues, necessitating transparent and understandable models. Additionally, the shift to digital credit scoring requires robust cybersecurity measures to protect sensitive data. Finally, the widespread use of AI in credit scoring could have unintended consequences for financial stability, such as mass defaults due to algorithmic errors. Careful monitoring and management of these systems are essential to prevent disruptions to the financial ecosystem.  
   
• Model Development, Scalability, and Talent Acquisition 
  AI models must effectively incorporate alternative data while ensuring interpretability to meet regulatory requirements. The "black box" nature of some AI algorithms adds complexity, necessitating transparency to align with compliance standards and build trust. Additionally, scaling infrastructure to process large volumes of data in real time is critical for operational efficiency. Companies must also address the talent gap by acquiring and retaining skilled AI and data science professionals while bridging the divide between technical expertise and business strategy to ensure effective implementation and alignment with organizational goals. 

Key Steps to Adapt a Data Driven Strategy for the Future

To adapt a data driven strategy for the future: 

1. Assess existing data structures and develop a clear data strategy  
   Evaluate your present data capabilities and find opportunities for growth. Create a clear plan for appropriately integrating AI and alternative data.
   
   
2. Invest in data infrastructure and validate alternative data  
   Invest in robust data infrastructure and build a solid basis for secure alternative data management and analysis. Prioritise data integrity, security, and real-time processing capabilities. 
   
   
3. Develop AI capabilities and ensure regulatory compliance 
   Develop AI-powered credit scoring models that use machine learning to analyse alternative data. Ensure these models are transparent, explainable, and comply with responsible AI regulations frameworks. 
    
4. Foster a data-driven culture and implement ethical AI practices 
   Encourage data literacy and data-driven decision-making at all levels.  
   Create ethical AI credit scoring criteria that eliminate biases.  
   Audit AI models continuously to ensure fair credit ratings across varied populations. 
   
   
5. Collaborate, partner and prioritise data security  
   Establish strategic partnerships with fintech companies, data vendors, and industry peers to promote AI, alternative data usage and share best practices.  
   Implement robust cybersecurity measures to protect sensitive alternative data and ensure compliance with data protection legislation.  
   

How Cedar Rose Helps You Master the Future of Credit Scoring  

Though AI and machine learning reveal hidden patterns in large unstructured datasets, they still require skilled data scientists and business intelligence professionals. Their skills ensure model accuracy, reduce overfitting, preserve stability and verify AI pattern’s relevance and explainability.  

At Cedar Rose, we understand the importance of AI and human skills working together.  
By combining innovation and expertise, we redefine credit scoring with innovative AI-driven solutions. Our Data Licensing solutions ensure complete data handling with powerful AI modelling. Moreover, our AI-powered CR Score, Auto Size Indicator (ASI), and Automated Credit Limit (ACL) algorithm analyse diverse data sources to produce complete and insightful credit reports.  
 
Furthermore, our CRiS Intelligence platform offers access to millions of corporate records worldwide. This enables businesses to optimise credit qualification, limit determination, and pricing strategies.  

Take charge of your credit scoring today.  
Contact us to learn more.

Sources

1. https://www.leewayhertz.com/ai-based-credit-scoring/ 
2. https://www.forbes.com/sites/tomcoughlin/2018/11/27/175-zettabytes-by-2025/  
3. https://www.experian.com/blogs/insights/2023-state-of-alternative-credit-data/  
4. https://www.fico.com/blogs/how-use-alternative-data-credit-risk-analytics  
5. https://www.finextra.com/blogposting/23326/looking-for-digital-footprint-how-alternative-data-is-becoming-a-new-goldmine  
6. https://trustdecision.com/resources/blog/alternative-credit-scoring-digital-transformation-in-banking-financial-inclusion